Lai, M.; Li, T.; Yan, F.; Li, J.; Raabe, D.: Revisiting o phase embrittlement in metastable b titanium alloys: Role of elemental partitioning. Scripta Materialia 193, pp. 38 - 42 (2021)
Lai, M.; Li, Y.; Lillpopp, L.; Ponge, D.; Will, S.; Raabe, D.: On the origin of the improvement of shape memory effect by precipitating VC in Fe–Mn–Si-based shape memory alloys. Acta Materialia 155, pp. 222 - 235 (2018)
Lai, M.; Li, T.; Raabe, D.: ω phase acts as a switch between dislocation channeling and joint twinning- and transformation-induced plasticity in a metastable β titanium alloy. Acta Materialia 151, pp. 67 - 77 (2018)
Zhang, J.; Tasan, C. C.; Lai, M.; Yan, D.; Raabe, D.: Partial recrystallization of gum metal to achieve enhanced strength and ductility. Acta Materialia 135, pp. 400 - 410 (2017)
Zhang, J.; Tasan, C. C.; Lai, M.; Zhang, J.; Raabe, D.: Damage resistance in gum metal through cold work-induced microstructural heterogeneity. Journal of Materials Science 50 (17), pp. 5694 - 5708 (2015)
Zhang, J.; Tasan, C. C.; Lai, M.; Zhang, J.; Raabe, D.: Damage Resistance through Hierarchical Microstructure Development on GUM Metal. Materials Science and Engineering (MSE2014), Darmstadt, Germany (2014)
Zhang, J.; Tasan, C. C.; Lai, M.; Springer, H.; Raabe, D.: Microstructural and Mechanical Characterization of Cold Work Effects in GUM Metal. 9th International Conference on Advances in Experimental Mechanics, Cardiff, UK (2013)
Zhang, J.; Raabe, D.; Lai, M.; Yan, D.; Tasan, C. C.: Site-preferential recrystallization and nano-precipitation to achieve improved mechanical properties. MRS Fall Meeting 2016, Boston, MA, USA (2016)
New machine learning framework makes metal recycling more sustainable without compromising on mechanical properties. Results now published in journal Science Advances.
Scientists of the Max-Planck-Institut für Eisenforschung pioneer new machine learning model for corrosion-resistant alloy design. Their results are now published in the journal Science Advances
In order to prepare raw data from scanning transmission electron microscopy for analysis, pattern detection algorithms are developed that allow to identify automatically higher-order feature such as crystalline grains, lattice defects, etc. from atomically resolved measurements.
The general success of large language models (LLM) raises the question if they could be applied to accelerate materials science research and to discover novel sustainable materials. Especially, interdisciplinary research fields including materials science benefit from the LLMs capability to construct a tokenized vector representation of a large…